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Ji-Jie Lin

dblp:412/4694 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0003-7142-3270ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 100%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing › music transcription
chord recognition
0.912025
Strumming in the Metaverse: A Deep-Learning-Enabled Virtual Air Guitar System in VR With Enhanced Chord Recognition and Simulated Pedal Effects · IEEE Trans. Multim. 2025
Audio and music processing
music performance
0.912025
Strumming in the Metaverse: A Deep-Learning-Enabled Virtual Air Guitar System in VR With Enhanced Chord Recognition and Simulated Pedal Effects · IEEE Trans. Multim. 2025
Immersive interaction
virtual reality
0.912025
Strumming in the Metaverse: A Deep-Learning-Enabled Virtual Air Guitar System in VR With Enhanced Chord Recognition and Simulated Pedal Effects · IEEE Trans. Multim. 2025

Methods — techniques the papers use, named apart from their topics

wavenet · 1.7knob difference loss · 1.7deep learning · 1.7FiLM · 1.7
YearPublicationVenuePosition
2025 Strumming in the Metaverse: A Deep-Learning-Enabled Virtual Air Guitar System in VR With Enhanced Chord Recognition and Simulated Pedal Effects
abstract
Virtual reality (VR) is increasingly capable and inexpensive, and VR devices have become indispensable in many domains, such as gaming, videoconferencing, education, and healthcare. VR has also been applied to music performance and learning. Virtual instruments, such as virtual pianos and drums, free users from the need to own physical forms of these (often bulky and expensive) instruments. VR devices enable users to enjoy music anytime and anywhere without constraints. Virtual concerts, including spatial audio simulations and reconstructions of historical performances, are becoming increasingly common. Previous studies have primarily examined virtual guitars in non-VR environments and air guitar chord recognition. However, systematic research on virtual air guitar systems in VR remains scarce. Virtual guitar games that are available on the market cannot recognize hand gestures accurately and thus cannot accurately identify the strumming patterns and chords played by the player. To overcome this problem, we propose a VR-based virtual air guitar system that can recognize 30 chords and various strumming techniques through deep learning and visual feedback. Employing a black-box approach, we combine WaveNet and FiLM to simulate electric guitar pedal effects with a knob difference loss mechanism, which simulates the turning of knobs on a guitar effects pedal, for enhanced accuracy.
Yi-Zeng Hsieh, Ji-Jie Lin, Mu-Chun Su, Wei-Jen Lin
IEEE Trans. Multim.2